SoroushVahidi/scidocs huggingface dataset:
Modeling and animating eye blinks
(2011)

Facial animation often falls short in conveying the nuances present in the facial dynamics of humans. In this article, we investigate the subtleties of the spatial and temporal aspects of eye blinks. Conventional methods for eye blink animation generally employ temporally and spatially symmetric sequences; however, naturally occurring blinks in humans show a pronounced asymmetry on both dimensions. We present an analysis of naturally occurring blinks that was performed by tracking data from high-speed video using active appearance models. Based on this analysis, we generate a set of key-frame parameters that closely match naturally occurring blinks. We compare the perceived naturalness of blinks that are animated based on real data to those created using textbook animation curves. The eye blinks are animated on two characters, a photorealistic model and a cartoon model, to determine the influence of character style. We find that the animated blinks generated from the human data model with fully closing eyelids are consistently perceived as more natural than those created using the various types of blink dynamics proposed in animation textbooks.
Emotion Detection From Text Documents
(2014)

Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.
Honesty on the Streets : A Natural Field Experiment on Newspaper Purchasing
(2008)

A publisher uses an honor system for selling a newspaper in the street. The customers are supposed to pay, but they can also pay less than the price or not pay at all. We conduct an experiment to study honesty in this market. The results show that appealing to honesty increases payments, whereas reminding the customers of the legal norm has no effect. Furthermore, appealing to honesty does not affect the behavior of the dishonest. These findings suggest that some people have internalized an honesty norm, whereas others have not, and that the willingness to pay to obey the norm differs among individuals. In a follow-up survey study we find that honesty is associated with family characteristics, self-esteem, social connectedness, trust in the legal system, and compliance with tax regulations.
A hybrid edge-cloud architecture for reducing on-demand gaming latency
(2014)

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.
Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices
(2016)

Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.
Combination features and models for human detection
(2015)

This paper presents effective combination models with certain combination features for human detection. In the past several years, many existing features/models have achieved impressive progress, but their performances are still limited by the biases rooted in their self-structures, that is, a particular kind of feature/model may work well for some types of human bodies, but not for all the types. To tackle this difficult problem, we combine certain complementary features/models together with effective organization/fusion methods. Specifically, the HOG features, color features and bar-shape features are combined together with a cell-based histogram structure to form the so-called HOG-III features. Moreover, the detections from different models are fused together with the new proposed weighted-NMS algorithm, which enhances the probable “true” activations as well as suppresses the overlapped detections. The experiments on PASCAL VOC datasets demonstrate that, both the HOG-III features and the weighted-NMS fusion algorithm are effective (obvious improvement for detection performance) and efficient (relatively less computation cost): When applied to human detection task with the Grammar model and Poselet model, they can boost the detection performance significantly; Also, when extended to detection of the whole VOC 20 object categories with the deformable part-based model and deep CNN-based model, they still show competitive improvements.
Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered
(2010)

Computer-based instruction (CBI) is becoming increasingly popular in the classroom, particularly because the latest technological advancements allow for visually rich and interactive environments. While the inherent nature of CBIs is often thought to engage learners, research examining the role of motivation in learning with these environments has resulted in mixed findings. These findings are further complicated by unique design characteristics of distinct CBIs. This literature review synthesizes research that has examined the role of theoretically-grounded constructs of motivation in the context of three popular CBIs, multimedia, hypermedia, and hypertext. Specifically, this literature review considered empirical studies that examined the effect of these CBIs on motivation, in addition to the effect of motivation on learning outcomes and the learning process within the context of these environments. The literature review concludes with a theoretical consideration of previous research and a discussion of a framework for future directions. 2010 Published by Elsevier Ltd.
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
(2010)

Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless ensemble methods in data mining improving accuracy through combining predictions synthesis lectures on data mining and knowledge discovery sources. Yeah, sources about the books from countries in the world are provided.
The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands
(2003)

Although the application of segmentation and predictive modeling is an important topic in the database marketing (DBM) literature, no study has yet investigated the extent of adoption of these techniques. We present the results of a Dutch survey involving 228 database marketing companies. We find that managers tend to rely on intuition and on the long-standing methods RFM and cross-tabulation. Our results indicate that the application of segmentation and response modeling is positively related to company and database size, frequency of customer contact, and the use of a direct channel of distribution. The respondents indicate that future research should focus on models applicable for Internet marketing, long-term effects of direct marketing, irritation from direct marketing offers, and segmentation and predictive modeling techniques. D 2002 Elsevier Science B.V. All rights reserved.
Design and analysis of an origami continuum manipulation module with torsional strength
(2017)

This paper presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, low-volume deployment, light weight, and inherently safe human interaction and collaboration. Each module has a mass of around 110 g and integrates the actuation, sensing, and control sub-systems necessary for operation. The origami structure has 7.311 Nm/rad (0.128 Nm/degree) torsional stiffness while being capable of bending in two directions and changing arc-length down to a fully collapsed state. A maximum contraction of 35 mm and bending angle of 35.5 degrees were achieved with 45 mm arc length. The module is capable of passively supporting a 1-kg mass at its tip, or 4 additional serially connected modules, bending approximately 6 degrees in the worst case. We also show that we can actively compensate for external moments by pre-compressing or pre-bending the module. We utilize an inverse kinematic control scheme and use it for both open and closed loop control following a circular trajectory. Our results indicate that the module motion follows the desired trajectory with an RMS error of 0.681 mm in the horizontal (x-y) plane and 0.373 mm in the z-axis with closed-loop control. We also assembled two origami modules in series and drove them independently, demonstrating the proof-of-concept of a modular origami continuum manipulator.
Bayesian optimization for learning gaits under uncertainty
(2015)

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments.
A Generative Approach to Audio-Visual Person Tracking
(2006)

This paper focuses on the integration of acoustic and visual information for people tracking. The system presented relies on a probabilistic framework within which information from multiple sources is integrated at an intermediate stage. An advantage of the method proposed is that of using a generative approach which supports easy and robust integration of multi source information by means of sampled projection instead of triangulation. The system described has been developed in the EU funded CHIL Project research activities. Experimental results from the CLEAR evaluation workshop are reported.
Position-aware activity recognition with wearable devices
(2017)

Reliable human activity recognition with wearable devices enables the development of human-centric pervasive applications.We aim to develop a robust wearable-based activity recognition system for real life situations where the device position is up to the user or where a user is unable to collect initial training data. Consequently, in this work we focus on the problem of recognizing the on-body position of the wearable device ensued by comprehensive experiments concerning subject-specific and cross-subjects activity recognition approaches that rely on acceleration data. We introduce a device localization method that predicts the on-body position with an F-measure of 89% and a cross-subjects activity recognition approach that considers common physical characteristics. In this context, we present a real world data set that has been collected from 15 participants for 8 common activities where they carried 7 wearable devices in different on-body positions. Our results show that the detection of the device position consistently improves the result of activity recognition for common activities. Regarding cross-subjects models, we identified the waist as the most suitable device location at which the acceleration patterns for the same activity across several people are most similar. In this context, our results provide evidence for the reliability of physical characteristics based cross-subjectsmodels. © 2017 Elsevier B.V. All rights reserved.
Tears or Fears? Comparing Gender Stereotypes about Movie Preferences to Actual Preferences
(2017)

This study investigated the accuracy of gender-specific stereotypes about movie-genre preferences for 17 genres. In Study 1, female and male participants rated the extent to which 17 movie genres are preferred by women or men. In Study 2, another sample of female and male participants rated their own preference for each genre. There were three notable results. First, Study 1 revealed the existence of gender stereotypes for the majority of genres (i.e., for 15 of 17 genres). Second, Study 2 revealed the existence of actual gender differences in preferences for the majority of genres (i.e., for 11 of 17 genres). Third, in order to assess the accuracy of gender stereotypes on movie preferences, we compared the results of both studies and found that the majority of gender stereotypes were accurate in direction, but inaccurate in size. In particular, the stereotypes overestimated actual gender differences for the majority of movie genres (i.e., 10 of 17). Practical and theoretical implications of these findings are discussed.
Rule extraction from autoencoder-based connectionist computational models
(2019)

1College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China 2Developmental Neurocognition Lab, Department of Psychological Sciences, Birkbeck, University of London, London, UK 3College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China Correspondence Juan Yang, College of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China. Email: jkxy_yjuan@sicnu.edu.cn Funding information National Natural Science Foundation of China, Grant/Award Number: (61402309); UK Economic and Social Research Council, Grant/Award Number: RES-062-23-2721 Summary Mapping problems are typical research topics related to natural language learning, and they include not only classification mappings but also nonclassification mappings, such as verbs and their past tenses. Connectionist computational models are one of the most popular approaches for simulating those mapping problems; however, their lack of explanatory ability has prevented them from being further used to understand the language learning process. Therefore, the work of extracting rational rules from a connectionist model is as important as simulating the mapping behaviors. Unfortunately, there is no available technique that can be directly applied in those computational models to simulate nonclassification problems. In this paper, an autoencoder-based connectionist computational model is proposed to derive a rule extraction method that can construct “If-Then” rational relations with high fidelity for nonclassification mapping problems. To demonstrate its generalizability, this computational model is extended to a modified version to address a multi-label classification mapping problem related to cognitive style prediction. Experiments prove this computational model's simulation ability and its explanatory ability on nonclassification problems by comparing its fidelity performances with those of the classical connectionist computational model (multilayer perceptron artificial neural network), and its similar ability on a multi-label classification problem (Felder-Silverman learning style classification) by comparing its prediction accuracy with those of other rule induction techniques.
Shape from periodic texture using the spectrogram
(1992)

Texture has long been recognized in computer vision as an important monocular shape cue, with texture gradients yielding information on surface orientation. A more recent trend is the analysis of images in terms of local spatial frequencies, where each pixel has associated with it its own spatial frequency distribution. This has proven to be a successful method of reasoning about and exploiting many imaging phenomena. Thinking about both shape-from-texture and local spatial frequency, it seems that texture gradients would cause systematic changes in local frequency, and that these changes could be analyzed to extract shape information. However, there does not yet exist a theory that connects texture, shape, and the detailed behavior of local spatial frequency. We show in this paper how local spatial frequency is related to the surface normal of a textured surface. We find that the Fourier power spectra of any two similarly textured patches on a plane are approximately related to each other by an affine transformation. The transformation parameters are a function of the plane’s surface normal. We use this relationship as the basis of a new algorithm for finding surface normals of textured shapes using the spectrogram, which is one type of local spatial frequency representation. We validate the relationship by testing the algorithm on real textures. By analyzing shape and texture in terms of the local spatial frequency representation, we can exploit the advantages of the representation for the shape-from-texture problem. Specifically, our algorithm requires no feature detection and can give correct results even when the texture is aliased.
Network traffic anomaly detection using clustering techniques and performance comparison
(2013)

Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Machine learning approaches are widely used to distinguish traffic flow outliers based on different anomalies with unique statistical characteristics. K-means clustering and Gaussian Mixture Model (GMM) are effective clustering techniques with many variations and easy to implement. Fuzzy clustering is more flexible than hard clustering and is practical for intrusion detection because of the natural treatment of data using fuzzy clustering. Fuzzy c-means clustering (FCM) is an iteratively optimal algorithm normally based on the least square method to partition data sets, which has high computational overhead. This paper proposes modifications to the objective function and the distance function that reduce the computational complexity of FCM while keeping clustering accurate. A combination of FCM clustering GMM, and feature transformation methods are proposed and a comparison of the related testing results and clustering methods is presented.
ElasticSwitch: practical work-conserving bandwidth guarantees for cloud computing
(2013)

While cloud computing providers offer guaranteed allocations for resources such as CPU and memory, they do not offer any guarantees for network resources. The lack of network guarantees prevents tenants from predicting lower bounds on the performance of their applications. The research community has recognized this limitation but, unfortunately, prior solutions have significant limitations: either they are inefficient, because they are not work-conserving, or they are impractical, because they require expensive switch support or congestion-free network cores.
 In this paper, we propose ElasticSwitch, an efficient and practical approach for providing bandwidth guarantees. ElasticSwitch is efficient because it utilizes the spare bandwidth from unreserved capacity or underutilized reservations. ElasticSwitch is practical because it can be fully implemented in hypervisors, without requiring a specific topology or any support from switches. Because hypervisors operate mostly independently, there is no need for complex coordination between them or with a central controller. Our experiments, with a prototype implementation on a 100-server testbed, demonstrate that ElasticSwitch provides bandwidth guarantees and is work-conserving, even in challenging situations.